{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import Series, DataFrame\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s1 = Series(np.random.randn(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   7.,    7.,   52.,  112.,  180.,  236.,  209.,  127.,   56.,   14.]),\n",
       " array([-3.41945958, -2.80158065, -2.18370173, -1.5658228 , -0.94794387,\n",
       "        -0.33006495,  0.28781398,  0.90569291,  1.52357183,  2.14145076,\n",
       "         2.75932969]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p0vX19cWaNWviW9/6Vjz44IPxxz/+seyRSvXaa6/FY489VvYYk25oaCi2bNkSDz74YLS2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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158cdf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115beff28>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mof6LX/xC6TqI6DocPtUJAJgzlePp45V50cYus6ZwKIPC05g+2mdlZeHAgQN4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\nREREGsFQJyIi0oj/DylugBUkY6eGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115cde160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s1.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1176c0b70>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIAMAYACBDACAAQQyAAAGEMgAABjg6nvI8XhcW7duVU9PjwYGBrR+/Xp95jOf0ZYtW+Tz\n+TRv3jw1NjYqL4+8BwAgE64C+ciRIyotLdWuXbv0wQcf6Jvf/KY+97nPKRwOa8mSJWpoaFB7e7uW\nL1+e7XoBAMhJrqawK1eu1EMPPSRJchxH+fn56u7uVmVlpSSpqqpKnZ2d2asSAIAc5yqQi4uLFQgE\nFI1GtWHDBoXDYTmOI5/Pl3o8EolktVAAAHKZ63NZv/vuu3rwwQdVW1urb3zjG9q1a1fqsVgsppKS\nkrRjzJ07R35/vtsSTCgrC3pdggnj9SEYKJrGSrwzW15nOvRhCH3IrAezYR+a6Wt0FcgXLlzQfffd\np4aGBt1yyy2SpAULFqirq0tLlixRR0eHli5dmnacvr5LblZvRllZUL29HAlI14dMTjA/02V6Iv1c\nRx+G0IfMe5Dr+9CR+8fxwtnVIeu9e/fq4sWL2rNnj0KhkEKhkMLhsJqbm7VmzRrF43HV1NS4GRoA\ngFnJ5ziO49XKZ/pfRsyQh6Trw0Qu2TZTMSMaQh+G0Acuv5g05TNkAACQXQQyAAAGEMgAABhAIAMA\nYACBDACAAQQyAAAGEMgAABhAIAMAYACBDACAAa4vLoHcNdEza3FWIgCYPGbIAAAYQCADAGAAgQwA\ngAEEMgAABhDIAAAYQCADAGAAgQwAgAEEMgAABnBiEACAZyZ6IqJ0qhddm9XxphMzZAAADCCQAQAw\ngEAGAMAAAhkAAAMIZAAADCCQAQAwgEAGAMCAnPoe8rZnu/T4/UvGvD2RMTJ97rZnuyQp42UnWs9E\nxhnt/pGvZ/i/mdR95Pg7uvPWG1K32/5wWiVzPpG678jxd5SXl6ev/9/1kqRf/v4t3bvys6nlLl6K\n654V86+4P5FwVBooSI1556036Je/f0uSdO/Kz+qXv39LeXm+K+oomfMJfRAdSD0uSaWBAl28FFci\n4Vxxf/Lne1d+VkeOv6OLl+KpWpLrvngpnho7kXBSPw8fMy/Pp5I5n7iixtJAgT6IDqSWz8vzKZFw\nlJ/n0+D/njN8vOQy96yYryPH30k9N7ncaMsPr2Xk8iPXP9byE5V8bibrmYjxXt94j7kdM1vrGP5+\nynTc5Hs2ef9EejjWssn7k+/lO2+9IbVtJY3cBpLbVnK8kdtB8vec3C6T7/nhYybHHb7dtf3hdOp9\nnNyujxx/54plk/Xds2K+JKXGv/PWG1LLjhx7+LY+fN8y0vBxh/880nj7wUyWdbNMtuRUIPdciI17\neyJjZPrciazDTT3pxhn+pfqeC7GPfck+ed9o/458/mhG7iQSCeeK+8ba4Yxcbvj94z1v5HLjrSeT\nnV1ymUxqHvnYaK9htH5I0uD//h1t5z/aa06Ms/xYtUyk9olKPjfT30+mxnt9boJyos9zuw4342by\nnh3LWMuO9r4da9sab6yR28HwWkfbRkYba7T3xlj1jbfu8ZYf73UNX2683+to+9mx9r2Z7JOztd/O\nBIesAQAwgEAGAMAAAhkAAAMIZAAADMipD3VJH/+QkpsriSSfc+xEz4y+cggAzEaj7ffHyoJsX21q\nMpghAwBgQM7NkLNtvL+egoGijJYDACAdZsgAABhAIAMAYACBDACAAVn9P+REIqHHHntMb731lgoK\nCvTEE0/o+uuvz+YqAADISVkN5JdfflkDAwM6ePCgTpw4oSeffFJPP/10Nlcx42Xrw198iAwAcktW\nD1m/9tprWrZsmSRp0aJF+sc//pHN4QEAyFk+x3GydjmUH/zgB1qxYoVuu+02SVJ1dbVefvll+f18\nuwoAgPFkdYYcCAQUi310qapEIkEYAwCQgawG8s0336yOjg5J0okTJzR//ugXkAYAAFfK6iHr5Kes\nT58+LcdxtGPHDn3605/O1vAAAOSsrAYyAABwhxODAABgAIEMAIABBPIkXLp0SevXr9fdd9+turo6\nvf/++16XNO0ikYgeeOAB3XPPPVqzZo1ef/11r0vy1EsvvaRNmzZ5Xca0SyQSamho0Jo1axQKhXT2\n7FmvS/LMyZMnFQqFvC7DM/F4XJs3b1Ztba1WrVql9vZ2r0vyxODgoB555BGtXbtW69at0+nTp9M+\nh0CehEOHDunzn/+8nn/+ed15551qaWnxuqRp99xzz2np0qVqa2vTzp079cMf/tDrkjzzxBNPaPfu\n3UokEl6XMu2Gn6Vv06ZNevLJJ70uyRMtLS169NFH1d/f73Upnjly5IhKS0u1f/9+Pfvss3r88ce9\nLskTR48elSQdOHBA4XBYP/7xj9M+hy8JT0JdXZ0GBwclSefPn1dJSYnHFU2/uro6FRQUSBr6i7Cw\nsNDjirxz88036/bbb9fBgwe9LmXacZa+IeXl5WpubtbDDz/sdSmeWblypWpqaiRJjuMoPz/f44q8\ncfvtt6u6ulpS5vlAIGfohRde0C9+8Ysr7tuxY4cWLlyoe++9V6dPn9Zzzz3nUXXTY7we9Pb2avPm\nzdq6datH1U2fsfpwxx13qKury6OqvBWNRhUIBFK38/Pzdfny5Vl3YqCamhqdO3fO6zI8VVxcLGno\nPbFhwwaFw2GPK/KO3+9XfX29XnrpJf30pz9N/wQHWfH22287X/3qV70uwxOnTp1y7rjjDufYsWNe\nl+K5v/71r044HPa6jGm3Y8cO57e//W3q9rJlyzysxlv//Oc/ne985ztel+Gp8+fPO9/61recF154\nwetSTPjXv/7lVFdXO7FYbNzl+D/kSdi3b59efPFFSUN/Fc7GQzNvv/22HnroIe3evTt1DnPMPpyl\nD0kXLlzQfffdp82bN2vVqlVel+OZF198Ufv27ZMkXXXVVfL5fMrLGz9yZ9fxpCz79re/rfr6eh0+\nfFiDg4PasWOH1yVNu927d2tgYEDbt2+XNHQ+cy65OfssX75cr7zyitauXZs6Sx9mp7179+rixYva\ns2eP9uzZI2now25FRUUeVza9VqxYoUceeUR33323Ll++rK1bt6btAWfqAgDAAA5ZAwBgAIEMAIAB\nBDIAAAYQyAAAGEAgAwBgAIEMAIABBDIAAAYQyAAAGPD/m/qegVgeGxAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115fe08d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(s1, bins=20, hist=True, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bbc9a90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Df/Gl0M9sJToOCeA/eiAYL0ZC4cByJgoDx6IFAADv0GsEJyFRjOYpCJzZCpZP\ni7jfmRqM5UzUQPLen2EreAuBtqk1U5sUn/ydOkM+fBjq1i2io1CUYzkTNZDj2fmQAgF4h13D06fi\nXM0pVdzvTA3EciZqAOlQBewvLoHe9AxovfuKjkOCHZs5sW5YLzYIRT2WM1ED2JcugVxZGdxqtlhE\nxyHBjDOaIdAmFZZPPwGqq0XHoSjGciaqr+pqOBYugOFwBm8NSQTAl54OqboKlo2fi45CUYzlTFRP\n9pX5wYuODBwMJCSIjkMm4e+aAQCwrv9QbBCKaixnovoIBOCY9yQMVQ1OaRMd5b+oEwzVAstH60RH\noSjGciaqB+u770DduQNa76tgnNFMdBwyE7sd/o4XwPJ1MaT9+0WnoSjFciaqK11Hwty/wZBleK8d\nJToNmZA/vRsAHrVN9cdyJqoj6+pVUL/ZCl+vK6G3biM6DpnQsXLm1DbVF8uZqC4MA85H82BIEqpH\nZ4tOQyYVaN8BemITWNd/CBiG6DgUhVjORHVgXb0KlpKvg1vNbdqKjkNmJcvwd+kKZe/PUHb8R3Qa\nikIsZ6LT9fut5jE5otOQydXsd17/gdggFJVYzkSnyfr+ali2boGvZya3mikkX9ej+515vjPVA8uZ\n6HToOhLm/IVbzXTajJQWCLRuA+uGQqCqSnQcijIsZ6LTYHv9VajflsDXuy/0tqmi41CU8F18KaTq\nKlg/4V2qqG5YzkShaBoS8mbDUFVUjx0nOg1FEV/3SwAEd4kQ1QXLmSgE+7IXoPy0C9rAIdBbtBQd\nh6JIoOMF0BNcsK55j6dUUZ2wnIlOxe0OXg3M4eB5zVR3qgp/twwoe3ZDKf1OdBqKIixnolNwLpwP\neX85vMNHwEhOFh2HolDN1PYaTm3T6WM5E9VC2rcPjqcfh94kCdXDR4iOQ1HKn9EdhizDxv3OVAcs\nZ6JaJPw9F3JlJapzrgecTtFxKEoZTZIQSOsI9csvIP16QHQcihIsZ6KTULaVwv7SCwic3Rpa1iDR\ncSjK+S6+BJKuw/rhWtFRKEqELGdd1zFjxgxkZ2djwoQJ2LVr1wnPqaqqQk5ODnbu3FnztREjRmDC\nhAmYMGECpk+fHt7URBGW8P/+BEnXUXXDJEBVRcehKOe7+FIA3O9Mpy/ku87atWuhaRry8/NRXFyM\nvLw8LFiwoGb51q1b8ec//xllZWU1X/N6vTAMA8uWLYtMaqIIsny0Dra178PXqQv8R99UiRpCb5sK\nPSUF1g/6y0fzAAAeYUlEQVTWAJoGWK2iI5HJhSznTZs2ITMzEwCQnp6OkpKS45ZrmoZ58+bh/vvv\nr/laaWkpqqqqMGnSJPj9fkydOhXp6emnfJ2mTZ1QVaU+YwiblJREoa8fbhxPPQQCwOwZgCTBcted\nSG6aELGXSk6Orf3YHE8ImZnA668jZetGYFDj7yrh+0F0CVnObrcbLper5rGiKPD7/VCPTvV17979\nhHXsdjsmT56MMWPG4Mcff8SUKVOwevXqmnVO5uBBT33yh01KSiLKy48IzRBOHE/92Fa8jCZbtkDr\n0xeeFq2Bisj8XCYnO1ERoe8tAscTmtL9ciS+/jqqli2Hu3vPsH7vUPh+YE6n+oARcp+zy+VCZWVl\nzWNd109ZsgDQvn17DB8+HJIkoX379khOTkZ5eXkdIhMJ4PEEb25htaJq3ETRaSjGBDpeAP2MZrC9\n+zbg84mOQyYXspwzMjJQWFgIACguLkZaWlrIb7py5Urk5eUBAMrKyuB2u5GSktLAqESR5Vw4H8ov\ne+Eddi2M5vx5pTCTZfh6XAH54EFYNnwkOg2ZXMhyzsrKgtVqRU5ODubMmYPp06ejoKAA+fn5ta4z\nevRoHDlyBGPHjsW9996L3NzckFvbRCJJ5eVwPPVY8IIjI0aLjkMxSruiFwDAVvCm4CRkdpJhmONq\n7KL3H8TKPoxjOJ66cT0wFY7nF8Mz5TZoQ4ZF7HWO4T5ac4vYeHQdTW6+AYCBAyU7AIsl/K9xEnw/\nMKcG7XMminXKjv/A/uLzCJx1FrQBg0XHoVj2+6ntIt7jmWrHcqa4lzBrBqRAANUTbuIFRyjitB6c\n2qbQWM4U1yyffQLb6nfg73ghfJf1EB2H4kDggguhJzeF7Z0CwO8XHYdMiuVM8UvXkfDnhwAAVTdN\nBiRJcCCKC4oCX4+ekH89AOv6D0SnIZNiOVPcsr31Oiybv4LWMxOBtI6i41Ac0a7qBwCw/+tlwUnI\nrFjOFJ+8XiTMnglDVVE9/gbRaSjOBM49D4G2qbCufgfSAd5Gkk7Ecqa45Fj6HJTdP8E7+GroZ7YS\nHYfijSRB65sFyeeD/fVXRKchE2I5U/zxeOB88jEYDge8o7NFp6E4pfW+CoaiwMapbToJljPFHcfz\niyGX74P36uEwmiSJjkNxykhOhr/7JbCUfA116xbRcchkWM4UVyT3ETiffhyG0wnvNSNEx6E45+2X\nBQCw/eslwUnIbFjOFFfszy2E/OsBVA8fAcMV2/eDJfPzZ1wMPSkZ9tdeAbxe0XHIRFjOFDekw4fg\nnPckdJcL3qHXiI5DBKgqtD5XQT54ELZVBaLTkImwnCluOBY9A7miAt5rRwEJCaLjEAEAtKzg9dwd\nCxcITkJmwnKm+OB2w7FwfnCrechQ0WmIauhnnw3fxZfCsmkj1C+/EB2HTILlTHHB8dILkA8ehHb1\ncMDhFB2H6DjeYdcCABzPzhechMyC5Uyxz+uFY8E/Ydjt8DbCvZqJ6srfuQsC7drD9vZbkPfsFh2H\nTIDlTDHPvjIfyi974R0wGEaTJqLjEJ1IklA97FpIgQAci58VnYZMgOVMsS0QgOPpx2GoKrzDrxWd\nhqhWvsze0JObwv7SC4DbLToOCcZypphme/stqN/vhNanH4xmzUXHIaqdxQLv4KshHz4Mx/IXRach\nwVjOFLsMA45/PglDluEdMUp0GqKQtEFDYNjtcDz1OODxiI5DArGcKWZZPvsEli2b4bv0cuhnnS06\nDlFIRpMkeK8eDmVfGRwvPCc6DgnEcqaY5XhmHoDfTlMhigbea0dCT0iA86nHILmPiI5DgrCcKSbJ\nP3wP6+p34D/3PAQuuFB0HKLTZrgS4R0+AvKvB3jVsDjGcqaY5Fj8DCTDCG41S5LoOER14h16DfTE\nJnDMfwpSxUHRcUgAljPFHOnwIdiXL4PerBl8V/QSHYeo7pxOeEeOhnz4MJzznhKdhgRgOVPMsb/0\nIuTKSngHDwVUVXQconrxDr4a+hnN4Hjmn5B//EF0HGpkLGeKLYEAHIufgWGzQxswWHQaovqz2VF1\n4yRIXi9cjzwoOg01spDlrOs6ZsyYgezsbEyYMAG7du064TlVVVXIycnBzp07T3sdokiwrnkPyp7d\n0HpfBSMxUXQcogbx9eoN/0WdYXvvXVjXrBYdhxpRyHJeu3YtNE1Dfn4+pk2bhry8vOOWb926FePG\njcPu3btPex2iSHEsWQggOCVIFPUkCZ4pt8GQZbgeuh+orhadiBpJyHLetGkTMjMzAQDp6ekoKSk5\nbrmmaZg3bx7OOeec016HKBKU73fAuv5D+C+4CHq79qLjEIWFntoueGGSXT/COe9J0XGokYQ8Wsbt\ndsPlctU8VhQFfr8f6tEDbbp3717ndU6maVMnVFWpU/hwS0mJrWnQuBvPnGUAAHX0SCQnm/+ezdGQ\nsS44ngi69WagqBAJTzyKhBvGARdcUOdvEXfvB1EuZDm7XC5UVlbWPNZ1/ZQlW991Dh4Uex3ZlJRE\nlJfHztV44m48Hg+aLVkCJDfF4c7dgQpzX5c4OdmJCpNnrAuOJ9IkWG65Awl5s+G7fhwqVn1QpzMR\n4u79IEqc6gNGyGntjIwMFBYWAgCKi4uRlpYW8gXrsw5RQ9jfWAn50CFoWQMBi0V0HKKw813WA1qf\nvrAUb4bzqcdEx6EIC/nRKysrC0VFRcjJyYFhGMjNzUVBQQE8Hg+ys7NPex2iiDEM2JcsDN59asAg\n0WmIIqZq8i1Qt34N56N58GYNQqBzF9GRKEIkwzAM0SEACJ+iiJVpkmPiaTzqpo1oOrgftMt6wPPg\nnxo5Wf2Yb9q0YTiexqNu3gTXrBnwX3ARDr63DrDbQ64TT+8H0aRB09pEZudYsggAoPH0KYoD/m7d\n4R04BOp338A182HRcShCWM4U1aQDB2B763UEzjob/s5dRcchahRVN01GoG0qHEsWwVrwpug4FAEs\nZ4pq9pdfhKRp8A66GpD540xxwmZH5R8fhGGzI/GeO3nt7RjEdzOKXoEAHEufg2Gzwde3n+g0RI1K\nb9MWnltuh3zkCJrcciPg9YqORGHEcqaoZf3gfSi7f4J2ZR8YCa7QKxDFGF/f/tCu6gdL8Wa4ZkwX\nHYfCiOVMUcv+/GIAPBCM4pvnljsQSG0Hx/OLYctfLjoOhQnLmaKS/MP3sH64Fv6OFyDQvoPoOETi\n2O2ofOBh6AkJSPzjPVC2fi06EYUBy5mikmPpEkiGETwQjCjO6a3OgucP0yB5q5F00zhIFQdFR6IG\nYjlT9Kmqgv1fy6AnJcF3RS/RaYhMwX/JZagekwPlp11IvGMKoOuiI1EDsJwp6tjeeh3ywYPQ+vM6\n2kS/V519PXzdMmBb+z6cc/8mOg41AMuZoo7j+UVHr6M9WHQUInNRFHjuuQ96ixZwPpoH69r3RCei\nemI5U1RRN2+CZfNX8He/BEaLFqLjEJmO0aQJKu9/GFBVJN5+My9QEqVYzhRVHEdPn/Ly9CmiWgU6\nnIuqW++EfOgQkm4aD3jMeRMPqh3LmaKG9OsB2N54DYFWZ8HftZvoOESmpvXLgnfAIKjfbAVuuw0w\nxw0I6TSxnClq2Je/BMlbDW3gEF5Hm+g0VN18G/znpgHLltVctIeiA9/hKDr4/XAsWQjDZofWr7/o\nNETRwWJB5QMPAUlJcD3yINSNn4tORKeJ5UzR4a23oOzZDe2qvjBctd+gnIiOZzRPAR55BAgE0OTm\nGyAdOCA6Ep0GljNFhyefBAB4rx4uOAhRFOreHdVjx0P5ZS8S7+b+52jAcibTU78uBjZsgK9bBvTW\nbUTHIYpK3pFj4OvaDbY178HxzDzRcSgEljOZnmPhAgCAd+g1gpMQRTFZhueeadCTmyLhLzOgfvWl\n6ER0CixnMjWprAy2N18D2rSBPz1DdByiqGYkN4XnnmnB/c+33ATpyGHRkagWLGcyNcfS5yBpGjBy\nJE+fIgoDf9du8I4cA+WnXXA9/IDoOFQLvtuReXk8cCxZBD3BBQwcKDoNUcyozr4e/nM6wL7iZVjf\nKRAdh06C5Uym5Xh5KeRfD0AbMhRwOETHIYodFgs89/wRhtWKxGl3QyorE52I/gfLmczJ54Nj/tMw\nbDaePkUUAXqbtqiacBPkXw8gcer/8fQqk2E5kynZXnsFys97oPUfCCMpSXQcopikDRkKX9d02Na8\nB/uyF0THod9hOZP56Dqc/3wChqKg+poRotMQxS5Zhuf/7oWe4ILrkemQv98pOhEdxXIm07GuXgV1\n+zb4ruwDI4X3bCaKJKN5c1TdegekKg+a3HkL4PeLjkQA1FBP0HUdM2fOxLZt22C1WjF79mykpqbW\nLP/www8xb948qKqKUaNG4brrrgMAjBgxAi6XCwDQunVrzJkzJ0JDoJhiGHA++SgMSUL1iNGi0xDF\nBV9mb2gbP4d1w0dwPvUYPFPvFx0p7oUs57Vr10LTNOTn56O4uBh5eXlYsCB4xSafz4c5c+Zg5cqV\ncDgcGDt2LPr27YvExEQYhoFly5ZFfAAUW6yrV8Gy+StoPXpCb9NWdByiuFF1y+1Qvy2B89E8aP2y\neM90wUJOa2/atAmZmZkAgPT0dJSUlNQs27lzJ9q2bYukpCRYrVZ0794dGzduRGlpKaqqqjBp0iRM\nnDgRxcXFkRsBxY5AAAlz/gJDllE9drzoNERxxXAlwnPXvZD8fiTeeQtQVSU6UlwLueXsdrtrpqcB\nQFEU+P1+qKoKt9uNxMTfbt+XkJAAt9sNu92OyZMnY8yYMfjxxx8xZcoUrF69Gqpa+8s1beqEqioN\nHE7DpKTE1q0Io248L70ElH4LDBqEJp07nrA4OdkpIFTkcDzmFpfj6d0TGDEC6htvIOXJvwFz50Y+\nWD1F3ftbHYUsZ5fLhcrKyprHuq7XlOz/LqusrERiYiLat2+P1NRUSJKE9u3bIzk5GeXl5WjVqlWt\nr3PwoKch42iwlJRElJcfEZohnKJuPJqGMx7+E2TVgsMjsmFUHP/zkJzsREWF2J+RcOJ4zC2ux5M9\nHomffwH58cdx6Mr+8F3RK7Lh6iHq3t9qcaoPGCGntTMyMlBYWAgAKC4uRlpaWs2yDh06YNeuXaio\nqICmafjyyy/RrVs3rFy5Enl5eQCAsrIyuN1upKSkNHQcFMPsLy2F8tMuaAMHw2jBI7SJhLHZ4bl7\nKiBJSLzrNkju6C/BaBRyyzkrKwtFRUXIycmBYRjIzc1FQUEBPB4PsrOz8eCDD2Ly5MkwDAOjRo1C\ny5YtMXr0aEyfPh1jx46FJEnIzc095ZQ2xTm3G87H/g7Dbkf16OtEpyGKe4HzO8I7cgzsK/ORMOMh\nuB97WnSkuCMZhjmu2SZ6iiJWpkmOiabxJMyaAec/n0D1dWNrPRAsrqcZowDHY271Go/PB9cDU6H+\n8D0OvfwKtKxBkQlXD9H0/nYqDZrWJookpfQ7OJ75JwItWqJ6JM9rJjINiwWeP0yDoVrguvcuSL8e\nEJ0orrCcSRzDgOuBqZD8flTdfBtgs4tORES/o6e2Q/X146HsK4PrgWmi48QVljMJY1uZD+unRfBd\nchn8l1wqOg4RnYR3+Aj4O14I+1uvw/bGStFx4gbLmYSQDlXA9eeHYdhsqLr5VtFxiKg2igLP3ffC\nsNnhuv9eyLt/Ep0oLrCcqfEZRvCXfH85qkdnQ2/RUnQiIjoFvdVZqLr5FsiHDqHJbZN5c4xGwHKm\nRmd75V+wv/Ea/Od3hJc3tyCKClq/AdB6XQnLxs/h/Eeu6Dgxj+VMjUr+fidcD06D4XDCc+99gCL2\nkq1EdJokCZ7b/g+BlmfC+cRcWDZ8JDpRTGM5U+Px+dDkjpshV1bCc9ud0FueKToREdVFQkLwdpKy\njMTbb4ZUViY6UcxiOVOjSZj5MCxfbYLWpy98V/YRHYeI6iGQdj6qJ9wEZV8ZkiZPADRNdKSYxHKm\nRuFYOB/ORc8g0DYVnim3i45DRA3gHX4ttMzesHzxGVwPPyA6TkxiOVPEWd99BwmPTIfetCncD88E\nnLF1Kz6iuCNJ8Nx5NwLtzoFj6XOwL3tBdKKYw3KmiFI3b0KTWycBVhsqH/4z7zhFFCtsdlQ++DD0\nxES4HpwGy6dFohPFFJYzRYy68XMkjbkG0LyonHY/Ah3OEx2JiMJIb3kmPH+cDhgGmkzIhvJNiehI\nMYPlTBFhKVyP5DHXQHK74bl7KvyXXCY6EhFFgL9LV3juvhfy4cNIyh4BedePoiPFBJYzhZ111dtI\nun40oPlQef9D8PW+SnQkIoogX2YfeCbdEjyCO3sEpPJy0ZGiHsuZwsfvh3POLDS5aRwgy6h8ZCb8\nl/UQnYqIGoE27BpUj7oO6vc7kTxiCORf9oqOFNVYzhQWUlkZksZcg4THH4XesiXcs/8Gf5d00bGI\nqBFVj5uI6mtGQN2+DcnDBkL+8QfRkaIWy5kaxjBge+VfOOOqK2At2gDtsh5wP/okAh3OFZ2MiBqb\nJKH6hsmoyhkH5addSB4+CMq2UtGpohLLmepN+fYbJF0zGE3+71ZIRw6jatIUeB54GEaCS3Q0IhJF\nkuDNvh5VN90M5b+/IHlwX1hXvS06VdRhOVOdKd+UIPG2yWjarxesn30C7bIeOPLUAniHXQtIkuh4\nRGQC3uEjUDn1fkg+H5JuvB7OObOAQEB0rKihig5AUcLrhfWDNbAvex62D9YAAAKp7VA14Ub4u18i\nOBwRmZEvszeOtGmLhL/NRsLjj8KyaROOPP409DZtRUczPZYz1Ur69QAsn30K6/vvwvb2W5APHwYA\n+C/shOqRo+HPuJhbykR0Snq79nD/4wk4n3wM1sJ1OCPzMrgfmYnqm6YAMidva8NyjneGAelQBeTy\ncig/7ISyYweU/2yD5asvoX73bc3T9GbNUX3NSPiu7IPAOR0EBiaiaGO4ElH50AxYPloHx5KFSJx+\nH+wrX4H7z7Phv5ynW54MyzkKSIcqIO/eDeXnPZB/2Qvp8GFIlUcgud2Q3G7Ibjck9xHA5wP8fkiB\nACADydUaEAgEHwf8v/t78I/k80E6+Cskv/+E1zSsNvi6dIX/wk7wd0lH4PyO/JRLRPUnSfD16Qt/\nejc4Fj8La9EGNB0+EN5+WfA8NAP+zl1FJzQVlrNZVFdD3V4K5dtvoH5TAuX7HVD27Ia8ZzfkI0fq\n9K0MRQEUBaosA7IcfCwrwXJVjn5NkmFYLNA7nAsjKRl6kyToLVtCP7s1Aq3bQD+zFWCxRGiwRBSv\njOSm8PzxQXiHXgP7y0th+2ANbB+sgZbZG1U3TYE2aAigspr4f6CxGQbkX/ZC/WZrsIi/LYH67TdQ\ndvwnuFX7+6c6nNBTUuBL6wg9pQX0lBTozZrDSEiA4XACTgcMuxOGwwHD4QiW6dGt2+RkJw5VeESM\nkIgopEDHC1A5aw7Ur4thW/kKrBs+gnXDRwicdTa8I8fAO/xa+Lt2i9vjWljOESQdOQxl+zaopd9B\n+WYr1KNlLFdUHPc8w+FA4LzzEWjXDoF25yCQ2g56mzY8X5iIYpskwd+1G/xdu0He/RNs774D6/oP\n4fznE3D+8wkE2qZCyxoILbMPfD17wUhKFp240UiGYRiiQwBAeXndpm7DLSUlsX4ZdB3yvjLIu3+C\nsuM/UEu/g7rtOyjbSqH8vOe4pxqSBP3MVgi0ax/8k9oOevtzoKe0CPunw+RkJypiaMuZ4zE3jsfc\nomo8Xi8sxV/B8snHsGz8HFJVFQDAkOXgMTBd0+Ho1QMHU89D4Ly0qC7slJTEWpeF3HLWdR0zZ87E\ntm3bYLVaMXv2bKSmptYs//DDDzFv3jyoqopRo0bhuuuuC7mO6RkGpEo3pF9/hVxxMPjfg79COngQ\n8oH9kH/eA2X3bii7d0He+zMkTTvhW+hnnAFf13TobVIRaNM2WMZtUwG7XcCAiIiihM0G32U94Lus\nB+DzBc8e+XoL1K+3QN1eCkvJ18DLL6Lp0afrzVMQOKcDAme2Ch430/JM6C1aBv+0PBNGcnJwV6Az\nAbBahQ6tLkKW89q1a6FpGvLz81FcXIy8vDwsWLAAAODz+TBnzhysXLkSDocDY8eORd++ffHVV1/V\nuk5jsK56G/Z/LQseuazrQEAH9KNHKOt68GhlPQAEdEiaN/jJTPOimccDqboaUnX1ab2OnpSMQNt2\nwX3BLVpAb3U2Am3aQm/bFoar9k9ERER0GiwWBC7shMCFnYCccUAgAHn3T2jyy0+o/m4blJ9/hvzz\nHqhffgGLrof8dobFEizqBBcMuz14nI6iwlBVQFV++7sSfBw8mPboWSqSFNwXPmJ0hAcdFLKcN23a\nhMzMTABAeno6SkpKapbt3LkTbdu2RVJSEgCge/fu2LhxI4qLi2tdpzZNmzqhqkq9BnGCjUXAe++e\nfJkkAUePZoaiADZbcGvWboecnBz8u8MBJCQAZ5wR/NO06W9/mjUDWrcGWreG7HCY+vqn0TvZc3Ic\nj7lxPOYWa+M5bg7S7wf27wf++1+grCz43337gv89cgSorATcbkiVlZAqK4OPDx8CdD247u//nOIS\no7YzWwC33BTxsQGnUc5utxsu128HJimKAr/fD1VV4Xa7kZj42xZiQkIC3G73KdepzcGDYdwfMiMX\n0rSHYEhysIDl3/23ln279drn7A1D1gip9z50k+J4zI3jMbfYH48FaNoq+OeCBn5zwwgW9NGylmAE\nv2YYMBKbAGH8/9igfc4ulwuVlZU1j3VdrynZ/11WWVmJxMTEU67TKCSJ08pERFR3khQ8z/poZ4k6\nYjrkrGxGRgYKCwsBAMXFxUhLS6tZ1qFDB+zatQsVFRXQNA1ffvklunXrdsp1iIiI6NRCbs5mZWWh\nqKgIOTk5MAwDubm5KCgogMfjQXZ2Nh588EFMnjwZhmFg1KhRaNmy5UnXISIiotPD85yPiv19MtGN\n4zE3jsfcOB5zOtU+ZzMfbExERBSXWM5EREQmw3ImIiIyGZYzERGRybCciYiITIblTEREZDIsZyIi\nIpNhORMREZmMaS5CQkREREHcciYiIjIZljMREZHJsJyJiIhMhuVMRERkMixnIiIik2E5ExERmQzL\nmYiIyGRYzv9j586d6N69O7xer+goDeLxeHD77bdj3LhxuPHGG1FWViY6UoMcOXIEt912G8aPH4/s\n7Gxs3rxZdKSwWLNmDaZNmyY6Rr3puo4ZM2YgOzsbEyZMwK5du0RHarAtW7ZgwoQJomOEhc/nw333\n3Yfrr78eo0ePxgcffCA6UoMEAgFMnz4dOTk5GDt2LLZv3y46UsSwnH/H7Xbjb3/7G6xWq+goDfbK\nK6/goosuwssvv4zhw4dj0aJFoiM1yPPPP4/LL78cL730EubMmYNZs2aJjtRgs2fPxty5c6Hruugo\n9bZ27Vpomob8/HxMmzYNeXl5oiM1yKJFi/CnP/0p6j+cH/Pvf/8bycnJWL58ORYvXoy//OUvoiM1\nyLp16wAAK1aswD333IPHH39ccKLIUUUHMAvDMPDII49g6tSpuOOOO0THabAbb7wRgUAAALB37140\nadJEcKKGufHGG2s+NAUCAdhsNsGJGi4jIwP9+/dHfn6+6Cj1tmnTJmRmZgIA0tPTUVJSIjhRw7Rt\n2xZPP/007r//ftFRwmLQoEEYOHAggOB7nKIoghM1TP/+/dGnTx8AsfG+dipxWc6vvvoqli5detzX\nzjrrLAwZMgQdO3YUlKr+Tjae3NxcdOnSBRMnTsT27dvx/PPPC0pXd6caT3l5Oe677z489NBDgtLV\nXW3jGTJkCD7//HNBqcLD7XbD5XLVPFYUBX6/H6oanW8tAwcOxJ49e0THCJuEhAQAwX+nu+++G/fc\nc4/gRA2nqioeeOABrFmzBk899ZToOJFjkGEYhtG/f39j/Pjxxvjx441OnToZ119/vehIYbNjxw6j\nX79+omM0WGlpqTFkyBBj/fr1oqOEzWeffWbcc889omPUW25urvHOO+/UPM7MzBSYJjx2795tjBkz\nRnSMsNm7d68xYsQI49VXXxUdJaz27dtn9OnTx6isrBQdJSKi8+NtBKxZs6bm73379sWSJUsEpmm4\nZ599Fi1btsS1116LhISEqJ/O2rFjB/7whz/giSeeiMrZjViVkZGBdevWYciQISguLkZaWproSPQ7\n+/fvx6RJkzBjxgz06NFDdJwGe/PNN1FWVoZbb70VDocDkiRBlmPz0CmWc4waNWoUHnjgAbz22msI\nBALIzc0VHalB5s6dC03T8Ne//hUA4HK5sGDBAsGpKCsrC0VFRcjJyYFhGFH/cxZrnnnmGRw+fBjz\n58/H/PnzAQQPerPb7YKT1c+AAQMwffp0jBs3Dn6/Hw899FDUjiUU3jKSiIjIZGJzPoCIiCiKsZyJ\niIhMhuVMRERkMixnIiIik2E5ExERmQzLmYiIyGRYzkRERCbz/wGkPbXqykBZlQAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bd1a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(s1, shade=True, color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   7.,    7.,   52.,  112.,  180.,  236.,  209.,  127.,   56.,   14.]),\n",
       " array([-3.41945958, -2.80158065, -2.18370173, -1.5658228 , -0.94794387,\n",
       "        -0.33006495,  0.28781398,  0.90569291,  1.52357183,  2.14145076,\n",
       "         2.75932969]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p0vX19cWaNWviW9/6Vjz44IPxxz/+seyRSvXaa6/FY489VvYYk25oaCi2bNkSDz74YLS2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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c143198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.plt.hist(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.rugplot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
